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Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis Journal of Visualized Experiments www.jove.com Video Article Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis Rekha Marimuthu1,2, Habib Francis1,2, Suat Dervish3, Stephen C.H. Li4, Heather Medbury*1,2, Helen Williams*1,2 1 Department of Surgery, Vascular Biology Research Centre, Westmead Hospital 2 Westmead Clinical School, Department of Surgery, The University of Sydney 3 Westmead Research Hub, Westmead Institute for Medical Research 4 Institute for Clinical Pathology and Medical Research, Westmead Hospital * These authors contributed equally Correspondence to: Heather Medbury at [email protected] URL: https://www.jove.com/video/57941 DOI: doi:10.3791/57941 Keywords: Immunology and Infection, Issue 140, Monocyte, immunology, flow cytometry, inflammation, monocyte gating, monocyte marker expression, macrophages, atherosclerosis Date Published: 10/17/2018 Citation: Marimuthu, R., Francis, H., Dervish, S., Li, S.C., Medbury, H., Williams, H. Characterization of Human Monocyte Subsets by Whole Blood Flow Cytometry Analysis. J. Vis. Exp. (140), e57941, doi:10.3791/57941 (2018). Abstract Monocytes are key contributors in various inflammatory disorders and alterations to these cells, including their subset proportions and functions, can have pathological significance. An ideal method for examining alterations to monocytes is whole blood flow cytometry as the minimal handling of samples by this method limits artifactual cell activation. However, many different approaches are taken to gate the monocyte subsets leading to inconsistent identification of the subsets between studies. Here we demonstrate a method using whole blood flow cytometry to identify and characterize human monocyte subsets (classical, intermediate, and non-classical). We outline how to prepare the blood samples for flow cytometry, gate the subsets (ensure contaminating cells have been removed), and determine monocyte subset expression of surface markers — in this example M1 and M2 markers. This protocol can be extended to other studies that require a standard gating method for assessing monocyte subset proportions and monocyte subset expression of other functional markers. Video Link The video component of this article can be found at https://www.jove.com/video/57941/ Introduction Monocytes are a type of white blood cells which play a major role in promoting and resolving inflammation. There are three main subsets of monocytes recognized, classical (~85%), intermediate (~5%), and non-classical (~10%) monocytes, which are characterized by their level of cluster of differentiation (CD)14 and CD16 expression1. The proportions of monocyte subsets can differ with the presence of disease, such as an increased proportion of intermediates in various inflammatory states2,3 including cardiovascular disease, where the level of intermediates is associated with clinical events4,5. Furthermore, in disease conditions, monocytes can also undergo functional changes, with many changes detectable by a difference in surface marker expression6,7. One such example is monocyte M1-skewing, an increase in markers associated with M1 macrophages, which has been observed in cardiovascular disease, diabetes, obesity, and metabolic syndrome7,8,9,10. Despite the popularity of flow cytometry to assess monocyte subset proportion and function, there is a considerable variability in sample preparation and subset gating between studies which makes it difficult to compare findings between such studies. Importantly, there is no consensus in the demarcation of monocyte subsets, yet a standardized approach is essential given the clinical significance of changes in subset proportions in several diseases. Part of the difficulty in gating arises from the fact that monocytes differentiate from the classical through the intermediate to the non-classical subset11 and as such, monocytes exist as a continuous spectrum rather than distinct populations12. Interestingly, Zawada et al. showed that using either a rectangular or trapezoid gating of the intermediate subset, both resulted in a higher intermediate subset that predicted a cardiovascular endpoint13. This highlights that, at least for calculating proportions, the key issue is applying a consistent gating strategy between different samples (and studies), rather than attempting to definitively discriminate between subsets. While definitive gating may be more important when assessing function, the change in marker expression between subsets is incremental12,14, and thus again, consistency in gating is perhaps key. As such, an objective gating method that reproducibly apportions the monocyte subsets between different samples is needed. The purpose of the method presented here is to gate monocyte subsets with a clear explanation and justification for the gating technique employed and assess the subsets for surface marker expression, thus providing a method which allows researchers to have confidence in the use of this technique when assessing different samples. Copyright © 2018 Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported October 2018 | 140 | e57941 | Page 1 of 10 License Journal of Visualized Experiments www.jove.com Protocol This study has been approved by the WSLHD Human Research Ethics committee (HREC) (approval AU RED HREC/15/WMEAD/289). 1. Sample Preparation for Whole Blood Flow Cytometry NOTE: As human blood is potentially infectious, the sample set-up should be performed in a biohazard hood. 1. Collect the blood samples from the participants into 3 mL ethylene diamine tetra acetic acid (EDTA) tubes. 2. Determine the white blood cell (WBC) count using a hematology analyzer or hemocytometer. 3. Dilute with phosphate buffered saline (PBS) (pH ~7.4) to adjust the concentration to ~5 x 106 WBC/mL. 4. Prepare sufficient master mix for the number of tubes (e.g., for 14 tubes, prepare 16x master mix) by combining 16x volumes of 50 µL blood, 0.75 µL anti CD14-V450, 0.5 µL anti CD16-APC, and 0.625 µL anti HLA-DR-PerCP. Vortex and pipette 51.9 µL of mix into each tube (Table ). 1 NOTE: Antibodies should be titrated to determine optimal staining concentrations for the fluorescent antibodies used. 5. Add surface marker (M1 and M2 or isotype control, phycoerythrin (PE) labelled) antibodies (example as per Table 2) and PE labelled markers for T cells (CD3), B cells (CD19), neutrophils (CD66b), and natural killer (NK) cells (CD56) (Table 3). Vortex and incubate for 30 min, 4 °C in the dark. NOTE: Markers for lymphocytes, neutrophils, and NK cells are included only for the validation of the gating method. 6. Add 250 µL of combined red blood cell lysis/WBC fixing solution, vortex gently immediately, and incubate for 10 min in the dark at 4 °C. 7. Add 250 µL of PBS and spin cells down at 260 x g for 10 min at room temperature. 8. Remove supernatant, re-suspend cells in 300 µL of 1% formaldehyde. NOTE: Formaldehyde is toxic. Use nitrile gloves and use in fume hood. 9. Store at 4 °C protected from light until analysis is performed. NOTE: Flow analysis is recommended to be done within 48 h of sample preparation. 2. Flow Cytometry 1. Check flow cytometer log to ensure facility staff have performed quality control checks. NOTE: To ensure consistency between analyses, instrument quality control and maintaining consistent target fluorescence intensities using control beads are recommended. 2. To set up the flow cytometry experiment, click on “New experiment then “new specimen” and “new tube” to add tubes. Select bivariate plots by clicking on the icon and use the dropdown menus to select the axis parameters. Ensure inclusion of a CD16/CD14 plot and a plot displaying a detector alongside time to monitor the acquisition. 3. Insert tube and click “Acquire”. Check the instrument voltage settings ensuring that detector signals are not off scale. 4. Observe cells falling in the monocyte gate of the CD14/CD16 plot. Set recording threshold to 5,000 events for the classical monocyte gate and click on “Record”. 5. Continue to record data for remaining tubes. After data for all tubes has been recorded, export flow data as .fcs files for analysis. NOTE: To ensure accuracy, single color compensation controls should be recorded. A compensation matrix can be calculated and applied to the data before analysis to account for spectral spill over15,16. 3. Monocyte Gating 1. Open files in the analysis software. Double click tube name and select parameters from the dropdown menus to visualize the cells on a forward scatter area FSC(A)/forward scatter height FSC(H) plot. Create a doublet exclusion gate by clicking on the polygon gate tool icon and enclosing the cells as in (Figure 1A). 2. Select the gated cells (by double clicking on the gated region) and in the new display box adjust dropdown menu parameters to display the cells on an FSC(A)/side scatter SSC(A) plot. Click on the rectangular gate icon and generously select the monocyte population based on forward and side scatter properties to exclude the majority of lymphocytes, NK cells, and granulocytes (Figure 1B). 3. Select the gated cells and redisplay on a CD14/CD16 plot, selecting the parameters by using the dropdown menus. Click on the polygon gate to select monocytes based on their characteristic “┐” shape (Figure 1C). 4. Select the gated cells and display the monocytes on a CD16/HLA-DR plot by using the dropdown menus to select parameters. Click on the 17 polygon gate to select the HLA-DR positive cells and exclude any remaining NK cells and neutrophils (Figure 1D). 5. Select the gated cells and display the HLA-DR positive cells on a CD14/HLA-DR plot using dropdown menus to select parameters. Click on the polygon gate and draw a gate to exclude the HLA-DR high/CD14 low cells (B cells express high levels of HLA-DR but not CD14) (Figure ). 1E NOTE: B cell contamination may occur and therefore should be investigated. If the non-classical population in Figure 1C is not distinct from the cells to its left, then contamination is likely. Step 3.5 can be skipped if B cells are not overlapping with non-classical monocytes.
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